ISSS608 Assignment Part 2

This post is a submission for ISSS608 Assignment 1, Vast Challenge 2 part 2

Yeo Chia Guan Andy true
07-27-2021 
Reading layer `Abila' from data source 
  `C:\yeochiaguan\DataViz\_posts\2021-07-27-isss608-assignment-1-part-2\data\Geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 3290 features and 9 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 24.82401 ymin: 36.04502 xmax: 24.90997 ymax: 36.09492
Geodetic CRS:  WGS 84

Vehicle Movement by Employment Type

When we look at the pathings by Employment Type, we see a different picture. As observed below, the Employment Type “Missing”, which represents trucks (as identified with id > 100), showed a peculiar route taken. Vehicle ID 107 could be seen travelling pass Pilau Park to Velestinou Boulevard. Vehicle ID 29 from Facilities could also be seen travelling to unique places during the period.

4.2 Finding Patterns from Patronage and Vehicle Data

Next, we’ll try and identify the possible owners of the credit card and loyalty card by filtering the location by date and time. For the below charts, we filter the vehicle data to show the vehicle locations by hour, specifically on 6 Jan 2014. The method applied was to identify the vehicle id which was present in on the map on a specific hour and cross-reference it to the paronage dataset to identify the credit card transaction made as well as the location.

Isolated credit card transactions

The below observations using isolated cases could be made using the data from 6 Jan 14:

time (hr) Vehicle ID Location last4ccnum
9 101 Kronos Pipe 9220
10 101 Maximum Iron 9220
10 107 Nationwide Refinery 9735
15 104 Abila Airport 8642
20 19 Shoppers 7688

The steps above was repeated for all dates to obtain the below list of identified vehicle id, tagged to a credit card number. The rest of the data was not complete as during the process, several of the credit cards were found to be used at a shop where a different vehicle was driven instead. hence the echnique was found to be lacking in terms of the accuracy.

Vehicle ID last4ccnum
1 2681
10 1321
12 2418
13 9735
14 5368
19 7688
21 8156
24 9152 1877
32 7688
34 2681
35 9551
101 9220
104 8642
105 7792
107 9735

5.0 Future Works

As part of the future work, the network visualisation could be explored to match the credit card transactions to the vehicle id to identify the owners.